1 Maps

1.1 Italy

set_config(use_proxy(url = "it000-surf.zone2.proxy.allianz:8080"))
# dt_pop_it <- fread("D:/Users/EUDZ040/R/002_Zoning/Data files/pop_it.csv", blank.lines.skip = T, nrows = 100)
#wine <- read_html("https://italianwinecentral.com/wine-production-in-italy-by-region/")

# access once and store and then read the stored file
# wine <- content(GET("https://italianwinecentral.com/wine-production-in-italy-by-region/", timeout(60)))
# wine %<>% 
#   html_table() %>% 
#   extract2(1) %>%
#   slice(-21)
# wine[,-1] %<>% map_df(parse_number)
# export(wine, "D:/Users/EUDZ040/R/002_Zoning/Data files/wine.csv")
wine <- fread("D:/Users/EUDZ040/R/002_Zoning/Data files/wine.csv", header=TRUE)

italy <- st_read("D:/Users/EUDZ040/R/002_Zoning/Data files/Limiti_2016_ED50_g/Reg2016_ED50_g/Reg2016_ED50_g.shp")
## Reading layer `Reg2016_ED50_g' from data source `D:\Users\EUDZ040\R\002_Zoning\Data files\Limiti_2016_ED50_g\Reg2016_ED50_g\Reg2016_ED50_g.shp' using driver `ESRI Shapefile'
## Simple feature collection with 20 features and 4 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 313361 ymin: 3934041 xmax: 1312106 ymax: 5220492
## epsg (SRID):    NA
## proj4string:    +proj=utm +zone=32 +ellps=intl +units=m +no_defs
# your data will need to have these region names
print(unique(italy$REGIONE))
##  [1] Piemonte              Valle D'Aosta         Lombardia            
##  [4] Veneto                Trentino-Alto Adige   Liguria              
##  [7] Friuli Venezia Giulia Emilia-Romagna        Toscana              
## [10] Umbria                Marche                Molise               
## [13] Lazio                 Abruzzo               Puglia               
## [16] Campania              Calabria              Sicilia              
## [19] Sardegna              Basilicata           
## 20 Levels: Abruzzo Basilicata Calabria Campania ... Veneto
levels(italy$REGIONE) <- wine$Region
WineLocation <- full_join(italy, wine, by = c("REGIONE" = "Region"))
## Warning: Column `REGIONE`/`Region` joining factor and character vector,
## coercing into character vector
plot_wine <- ggplot(data = WineLocation, aes(fill = `2016`)) + 
  geom_sf() + theme_fivethirtyeight() + guides(fill = guide_colorbar(barwidth = 8, barheight = 1))

1.1.1 Viridis color palettes

plot_list <- list()

viridis_pal_names <- c('viridis', 'magma', 'inferno', 'plasma', 'cividis')
n_names = length(scico_pal_names)

for (i in seq(1,5)) {
  plot_list[[i]] <- plot_wine + scale_fill_viridis(option=viridis_pal_names[i]) + ggtitle(viridis_pal_names[i])  
}

Grid them, grid’em all

do.call(plot_grid, c(plot_list, ncol=3))

1.1.2 Scico color palettes

plot_list <- list()

scico_pal_names <- scico::scico_palette_names()
n_names = length(scico_pal_names)

for (i in seq(1,n_names)) {
  plot_list[[i]] <- plot_wine + ggtitle(scico_pal_names[i]) + scale_fill_scico(palette = scico_pal_names[i])
}

Grid them, grid’em all

do.call(plot_grid, c(plot_list, ncol=3))

1.2 US

# Load the USmap
unemp <- read.csv("http://datasets.flowingdata.com/unemployment09.csv",
                  header = FALSE, stringsAsFactors = FALSE)
names(unemp) <- c("id", "state_fips", "county_fips", "name", "year",
                  "?", "?", "?", "rate")
unemp$county <- tolower(gsub(" County, [A-Z]{2}", "", unemp$name))
unemp$county <- gsub("^(.*) parish, ..$","\\1", unemp$county)
unemp$state <- gsub("^.*([A-Z]{2}).*$", "\\1", unemp$name)
county_df <- map_data("county", projection = "albers", parameters = c(39, 45))

# Build the choropleth and the states borders
names(county_df) <- c("long", "lat", "group", "order", "state_name", "county")
county_df$state <- state.abb[match(county_df$state_name, tolower(state.name))]
county_df$state_name <- NULL
state_df <- map_data("state", projection = "albers", parameters = c(39, 45))
choropleth <- merge(county_df, unemp, by = c("state", "county"))
choropleth <- choropleth[order(choropleth$order), ]

# The base plot
plot_county <- ggplot(choropleth, aes(long, lat, group = group)) +
  geom_polygon(aes(fill = rate), colour = alpha("white", 1 / 2), size = 0.01) +
  geom_polygon(data = state_df, colour = "white", fill = NA, size = 0.025) +
  coord_fixed() +
  theme_minimal() +
  ggtitle("US unemployment rate by county") + theme_fivethirtyeight() +
  theme(axis.line = element_blank(), axis.text = element_blank(),
        axis.ticks = element_blank(), axis.title = element_blank())

1.3 Viridis color palettes

plot_list <- list()

viridis_pal_names <- c('viridis', 'magma', 'inferno', 'plasma', 'cividis')
n_names = length(scico_pal_names)

for (i in seq(1,5)) {
  plot_list[[i]] <- plot_county + scale_fill_viridis(option=viridis_pal_names[i]) + ggtitle(viridis_pal_names[i])  
}

Grid them, grid’em all

do.call(plot_grid, c(plot_list, ncol=2))

1.4 Scico color palettes

plot_list <- list()

scico_pal_names <- scico::scico_palette_names()
n_names = length(scico_pal_names)

for (i in seq(1,n_names)) {
  plot_list[[i]] <- plot_county + ggtitle(scico_pal_names[i]) + scale_fill_scico(palette = scico_pal_names[i])
}

Grid them, grid’em all

do.call(plot_grid, c(plot_list, ncol=3))

2 Volcano

2.1 Viridis color palettes

rm(volcano)
## Warning in rm(volcano): oggetto "volcano" non trovato
volcano <- data.frame(
  x = rep(seq_len(ncol(volcano)), each = nrow(volcano)),
  y = rep(seq_len(nrow(volcano)), ncol(volcano)),
  height = as.vector(volcano)
)
plot_volcano <- ggplot(volcano, aes(x = x, y = y, fill = height)) + 
  geom_raster() + theme_fivethirtyeight()
plot_list <- list()

viridis_pal_names <- c('viridis', 'magma', 'inferno', 'plasma', 'cividis')
n_names = length(scico_pal_names)

for (i in seq(1,5)) {
  plot_list[[i]] <- plot_volcano + scale_fill_viridis(option=viridis_pal_names[i]) + ggtitle(viridis_pal_names[i])
}

Grid them, grid’em all

do.call(plot_grid, c(plot_list, ncol=3))

2.2 Scico color palettes

plot_list <- list()

scico_pal_names <- scico::scico_palette_names()
n_names = length(scico_pal_names)

for (i in seq(1,n_names)) {
  plot_list[[i]] <- plot_volcano + ggtitle(scico_pal_names[i]) + scale_fill_scico(palette = scico_pal_names[i])
}

Grid them, grid’em all

do.call(plot_grid, c(plot_list, ncol=3))

3 Line charts

NB: Those color schemes are best suited to continuous scales

t_H = 5
N_ticks = 1000
increment = rep(t_H/N_ticks, N_ticks)
S0 = 1
sigma = 0.025
mu = 0.025
# because Iprefer Google, I'll increase its trend
dt_stocks <- data.table(time = cumsum(increment),
                 AAPL = S0*exp(cumsum((mu-sigma*sigma/2)*increment + sigma*sqrt(t_H/N_ticks)*rnorm(N_ticks))),
                 AMZN = S0*exp(cumsum((mu-sigma*sigma/2)*increment + sigma*sqrt(t_H/N_ticks)*rnorm(N_ticks))),
                 GOOGL = S0*exp(cumsum((1.5*mu-sigma*sigma/2)*increment + sigma*sqrt(t_H/N_ticks)*rnorm(N_ticks))),
                 SAS = S0*exp(cumsum((-mu-sigma*sigma/2)*increment + sigma*sqrt(t_H/N_ticks)*rnorm(N_ticks)))
                 )
dt_stocks <- melt(dt_stocks ,  id.vars = 'time', variable.name = 'series')

d <- ggplot(data = dt_stocks, aes(x = time, y = value)) + geom_line(aes(colour = series), size = 1) + theme_fivethirtyeight()

3.1 Viridis color palettes

plot_list <- list()

viridis_pal_names <- c('viridis', 'magma', 'inferno', 'plasma', 'cividis')
n_names = length(scico_pal_names)

for (i in seq(1,5)) {
  plot_list[[i]] <- d + scale_colour_viridis_d(option=viridis_pal_names[i]) + ggtitle(viridis_pal_names[i])  
}

Grid them, grid’em all

do.call(plot_grid, c(plot_list, ncol=2))

3.2 Scico color palettes

plot_list <- list()
n_names = length(scico_pal_names)

for (i in seq(1,n_names)) {
  plot_list[[i]] <- d + ggtitle(scico_pal_names[i]) + scico::scale_colour_scico_d(palette = scico_pal_names[i])
}

Grid them, grid’em all

do.call(plot_grid, c(plot_list, ncol=3))

4 Categorical variables

Taking the Tidyverse example

txsamp <- subset(txhousing, city %in%
  c("Houston", "Fort Worth", "San Antonio", "Dallas", "Austin"))
d <- ggplot(data = txsamp, aes(x = sales, y = median)) + geom_point(aes(colour = city)) + theme_fivethirtyeight()

4.1 Viridis color palettes

plot_list <- list()

viridis_pal_names <- c('viridis', 'magma', 'inferno', 'plasma', 'cividis')
n_names = length(scico_pal_names)

for (i in seq(1,5)) {
  plot_list[[i]] <- d + scale_colour_viridis_d(option=viridis_pal_names[i]) + ggtitle(viridis_pal_names[i])  
}

Grid them, grid’em all

do.call(plot_grid, c(plot_list, ncol=2))

4.2 Scico color palettes

plot_list <- list()
n_names = length(scico_pal_names)

for (i in seq(1,n_names)) {
  plot_list[[i]] <- d + ggtitle(scico_pal_names[i]) + scico::scale_colour_scico_d(palette = scico_pal_names[i])
}

Grid them, grid’em all

do.call(plot_grid, c(plot_list, ncol=3))